Characterizing the morphology of vasculature in digital pathology is a useful step in defining the microenvironment within brain tissue samples. In particular, understanding the geometry of vessel configuration and its changes during a disease may provide insight into the progression of neuropathological degenerative diseases such as Alzheimer's disease.
Deep learning requires abundant training data for tuning the large number of parameters of the various inherent models. If a certain class is imbalanced then the classification models could become prone to biased outcomes. However, acquisition of natural training samples is a time consuming and labor intensive process.